Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2016

AUTHORS

Luis Martí , Arsene Fansi-Tchango , Laurent Navarro , Marc Schoenauer

ABSTRACT

This paper presents the Voronoi diagram-based evolutionary algorithm (VorEAl). VorEAl partitions input space in abnormal/normal subsets using Voronoi diagrams. Diagrams are evolved using a multi-objective bio-inspired approach in order to conjointly optimize classification metrics while also being able to represent areas of the data space that are not present in the training dataset. As part of the paper VorEAl is experimentally validated and contrasted with similar approaches. More... »

PAGES

697-706

References to SciGraph publications

  • 2007-12. Immune system approaches to intrusion detection – a review in NATURAL COMPUTING
  • 2004-01. Support Vector Data Description in MACHINE LEARNING
  • 2002-03. Compact Unstructured Representations for Evolutionary Design in APPLIED INTELLIGENCE
  • 2004. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors in GENETIC AND EVOLUTIONARY COMPUTATION – GECCO 2004
  • Book

    TITLE

    Parallel Problem Solving from Nature – PPSN XIV

    ISBN

    978-3-319-45822-9
    978-3-319-45823-6

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-45823-6_65

    DOI

    http://dx.doi.org/10.1007/978-3-319-45823-6_65

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1005929034


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